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Please use this identifier to cite or link to this item: http://hdl.handle.net/10059/317
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Title: Relation discovery from web data for competency management.
Authors: Zhu, Jianhan
Goncalves, Alexandre L.
Uren, Victoria
Motta, Enrico
Pacheco, Roberto
Eisenstadt, Marc
Song, Dawei
Keywords: Relation discovery
Clustering
Named entity recognition
Issue Date: Dec-2007
Publisher: IOS Press
Citation: ZHU, J., GONCALVES, A. L., UREN, V. S., MOTTA, E., PACHECO, R., EISENSTADT, M. and SONG, D., 2007. Relation discovery from web data for competency management. Web Intelligence and Agent Systems, 5 (4), pp. 405-417.
Abstract: In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (COmmunity Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.
ISSN: 1570-1263
Appears in Collections:Journal articles (Computing)

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